tfd_zinb

Implementation of a zero-inflated negbinom distribution for TFP

Allows for the specification of semi-structured deep distributional regression models which are fitted in a neural network as
proposed by Ruegamer et al. (2023) <doi:10.18637/jss.v105.i02>.
Predictors can be modeled using structured (penalized) linear effects, structured non-linear effects or using an unstructured deep network model.

David Ruegamer

deepregression

Fitting Deep Distributional Regression

Christopher Marquardt

Laetitia Frost

Florian Pfisterer

Philipp Baumann

Chris Kolb

Lucas Kook

tfd_zinb function

<dl><dt>mu, r</dt>
<dd>parameter of the negbin_ls distribution</dd>
<dt>probs</dt>
<dd>vector of probabilites of length 2 (probability for poisson and
probability for 0s)</dd></dl>

Arguments

Implementation of a zero-inflated negbinom distribution for TFP — tfd_zinb

<dl>

<dt>mu, r</dt>
<dd>parameter of the negbin_ls distribution</dd>


<dt>probs</dt>
<dd>vector of probabilites of length 2 (probability for poisson and
probability for 0s)</dd>

</dl>

tfd_zinb: Implementation of a zero-inflated negbinom distribution for TFP

Description

Usage

Arguments